Detection and segmentation of brain tumors in magnetic resonance images is of great importance in medical diagnosis. Reliable and precise tumor segmentation provides helpful information for surgical planning and therapy accessing. The tumor segmentation can also be used for general modeling of a pathological brain and the construction of pathological brain atlases. However, manual segmentation of brain tumor images commonly used in clinics is time-consuming, labor-intensive and subjective to considerable variation in intra- and inter-rater performance. Accordingly, a method for fully automatic brain tumor segmentation is desirable. Diligent efforts have been made to achieve time-efficient, accurate, and reproducible tumor segmentation. It however remains a challenging task to achieve robust segmentation as brain tumors differ much in appearance, location, size, and shape.
Recently, graph based image segmentation methods have been used for medical image segmentation, such as graph cut, random walks, and the like. The graph cut segments an image using region and edge based information, wherein the region based information is achieved by calculating the probability of each pixel belonging to the foreground/background, and the edge based information is measured by the feature similarity between pixels, including image intensity similarity and spatial proximity. Wels et al., “Method and System for Brain Tumor Segmentation in 3D Magnetic Resonance Images”, U.S. patent Application Publication, Pub. No.: US 2010/0027865 utilizes the PBT (probabilistic boosting tree) classifier and graph cut comprehensively to segment a brain tumor, in which the PBT classifier achieved from the training dataset is used to provide the measurement of region based information and the image intensity similarity is used as the edge based information. However, the supervised PBT classifier relies on the image consistency between training images and the image to be segmented, and its performance may be degraded by image noise, MR image bias field and inter-subject image variability. The region based information totally from the trained statistical information is not always reliable, and the unreliability will lead to inaccurate graph cut segmentation results. Furthermore, the edge based information measured only between neighboring voxels is not robust to the image noises, and may affect the performance for the detection of a tumor boundary.